A person viewing an image will not attend to the entire content of the image, but will selectively look at regions within the image. A professional-grade image, photograph or painting, will guide the gaze of the onlooker towards a region of interest with compositional techniques such as selective focus, colour and luminance contrast, and positioning of elements in a scene captured by the image.
Not all image practitioners are of equal proficiency. Further, the scene captured within an image cannot necessarily be controlled. As a result, a large number of images incorporate distracting elements that hinder an intended photographic output. Such image “distractors” may be thought of as unwanted, unnecessary, elements in an image which attract the attention of an observer away from an originally intended (or main) subject or region of interest. Distracting elements in an image may include artefacts.
Removing distracting elements is typically a two-step process involving detection of an image region as a distracting element (or distractor), and removal, or attenuation of that distracting element (or distractor). However, distracting elements can vary greatly in their statistics and appearance. Removing distracting elements requires knowledge of how an image would look without the distracting elements.
Most conventional methods of reducing visibility of distracting elements focus on removing highly specific cases such as noise, dust, or scratches. In these instances, a model of the distracting elements (or distractors) is typically built and employed to identify the artefacts in the image. Once identified, the same model is used to infer a distractor-free image.
When large regions of an image are identified as distracting elements, inpainting methods may be employed to reconstruct an artefact-free version of the image. However, inpainting methods are notoriously unreliable and computationally costly.
An important shortcoming of objective-based distracting element attenuation methods is that such methods decrease magnitude of distracting elements in an objective colour space (e.g., peak signal to noise ratio (psnr) or International Commission of Illumination (CIE) Delta E) which often have very little perceptual significance. However, in photography or any time a human user/observer is the intended viewer, distractors are of perceptual nature. The impact and influence of distracting elements on perceived image quality varies in a subjective fashion.
Most conventional methods that attempt to address the distracting element problem in images do so by sidestepping the problem. For example, the subject, or region of interest of the image is identified and what is not part of the subject is treated as “noise” (or background). Non-subject parts of the image then have their visibility reduced uniformly by being cropped out or blurred. While such methods may reduce the visibility of distracting elements (or distractors), such methods do not necessarily improve perceived quality of the image. Generally, alterations are so large and widespread that the effectiveness of such methods, from a perceptual standpoint is highly image, content, and observer dependent.
Thus, a need clearly exists for an improved method of identifying a distracting element in an image.